Conditional Variational Autoencoder
A demonstration of VAE
Project Description
In this project, I implemented a variational autoencoder (VAE), consisting of an encoder and decoder. The VAE was trained using a cross-entropy loss and a regularization term to enforce a prior distribution on learned variables. To improve generated image quality I replaced reconstruction loss with perceptual loss. I also extracted features from VGG16 for comparison in feature space and performed interpolation between samples in latent space and visualized translations within and across classes.
Key Features:
- Implemented conditional variational autoencoder.
- Trained VAE on image reconstruction task.
- Analyzed and visualized latent space interpolations.
Tools and Technologies:
- Python
- PyTorch
Source Code
The complete source code for this project is available on GitHub.